Mohamed K.
Data Engineer
At Weight Watchers, Mohamed improved ETL reliability by implementing CI/CD pipelines and automated monitoring systems. At Swvl, he developed a fraud detection system that resulted in significant cost savings and automated the migration of databases into BigQuery. Earlier in his career at Eventum IT Solutions, he worked as a full-stack developer, where he enhanced system performance and improved user insights.
With a strong foundation in modern data technologies and a proven track record of innovative problem-solving, Mohamed is a valuable asset for any organization seeking expertise in complex data engineering initiatives.
Main expertise
Docker3 years
Java2 years
SQL4 years
Snowflake2 yearsExperience3
Data Warehouse Engineer
- Improved data integration, monitoring, and automation processes by implementing CI/CD pipelines, upgrading service environments, and optimizing ETL workflows.
- Utilized cloud orchestration tools to manage the full data life cycle, from extraction to normalization and injection into Snowflake and Kafka.
- Developed Kubernetes-based monitoring jobs using Datadog, Snowflake, Python, Prefect Dataflow Automation, and PagerDuty to track and resolve data lag and missing records in ETL jobs, significantly improving data reliability and system stability.
- Set up and automated CI/CD pipelines for multiple services using GitHub Actions and Python scripts, enhancing deployment efficiency and reducing downtime.
- Led the upgrade of Python versions across services from 3.9 to 3.11, ensuring compatibility, performance improvements, and security enhancements.
- Migrated from dbt Cloud to dbt Open Source, designing and implementing a scalable, modular architecture for Snowflake’s data warehouse, which included:
- Automated metadata-driven pipelines and robust CI/CD workflows using GitHub Actions and dbt tests.
- Optimized query performance through incremental models, materializations, and strategic partitioning.
- Reduced onboarding time for new data models, improved execution times, conducted team training sessions, and enabled the analytics team to deliver insights faster and more efficiently.
MySQLDocker
Python
Apache Kafka
Kubernetes
Data Engineer
- Worked on a real-time engine that captured live changes in data and enabled automated business decision-making.
- Developed ETL data pipeline projects focused on efficiently moving data into the data warehouse.
- Built a real-time fraud detection system that identified and blocked fraudulent users creating multiple fake accounts to exploit promo codes, saving approximately half a million pounds per month. Technologies used included Debezium, Kafka, Flink, and Java.
- Implemented automated data migration pipelines to transfer data from backend databases (MongoDB, PostgreSQL, MySQL) into BigQuery, handled schema changes seamlessly, and ensured data consistency. Technologies used included Debezium, Kafka, Flink, Airflow, Java, Python, and React.js.
JavaScript
React.js
MongoDBDocker
PostgreSQLSofware Engineer
- Developed a Network Management System that delivered services such as network monitoring, performance analysis, configuration management, and remote device control based on specified trigger events.
- Implemented priority-based job scheduling to enhance system throughput and efficiency.
- Developed user activity tracking features to monitor system issues and analyze user behavior and preferences for improved insights.
MySQLJavaScript
RedisJavaPythonEngineering excellence
All Data Engineers who have applied to Proxify are scored from 0 to 300 on engineering excellence, one of the five parameters we evaluate. This score reflects engineering excellence only, based on interviews, take-home assignments, live coding sessions, and/or on-the-job performance reviews. The curve shows how all evaluated Data Engineers are distributed across that range, where our acceptance threshold for this parameter sits, and where Mohamed stands.
Score 176 · Top 16% of engineers
Education
Stop browsing.
Get matched faster.
Talk to an expert and get tailored matches from our network in just 2 days.
A network of over 6,000+ tech experts
Get matched with perfect-fit talent in 2 days on average
Hire quickly and easily with 94% match success